tf.layers.dropout 和tf.nn.dropout区别-优快云博客

本文详细解析了TensorFlow中tf.layers.dropout与tf.nn.dropout的区别。tf.nn.dropout使用keep_prob参数定义保留概率，而tf.layers.dropout则使用rate参数定义丢弃率，并且提供了一个training参数来区分训练与推理阶段的行为。

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tf.layers.dropout 和tf.nn.dropout区别

The only differences in the two functions are

tf.nn.dropout has parameter keep_prob: “Probability that each element is kept”
tf.layers.dropout has parameter rate: “The dropout rate”
Thus, keep_prob = 1 - rate as defined here
The tf.layers.dropout has training parameter: “Whether to return the output in training mode (apply dropout) or in inference mode (return the input untouched).”

tf.layers 是高层API，tf.nn是低层API

tf.layers is a higher-level wrapper, and tf.nn.dropout is from TensorFlow’s low-level library. tf.nn.dropout is there since the first public release of TensorFlow (version 0.6?), while tf.layers.dropout is there since about version 1.0 or so. As far as I know, the community develops new cool stuff in tf.contrib, which interfaces are likely to change. After a while, these are then transfered to tf.layers as soon as the interfaces (params, param-names, etc) are stable

参考：https://stackoverflow.com/questions/44395547/tensorflow-whats-the-difference-between-tf-nn-dropout-and-tf-layers-dropout